Medical image processing method and apparatus for discriminating body parts

ABSTRACT

An image processor wherein a radiation image forming device forms a radiation image corresponding to the radiation amount transmitted through an object, a discriminator discriminates at least one of the region of the object and the radiographing orientation for a radiation image, and an image processing condition memorizing device memorizes each of a plurality of image processing conditions in accordance with each of the regions of an object, each of the directions of radiographing, or each of the combinations of both of these. At least one image processing condition is displayed, and an image processing condition selects an arbitrary image processing condition out of the displayed image processing conditions. At least one image processing condition is read out from the image processing condition memorizing device based on the result of discrimination obtained by the discriminator and is displayed, and the selecting of an arbitrary image processing condition out of the displayed image processing conditions is accepted.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a divisional of U.S. patent application Ser. No. 09/819,400,filed Mar. 28, 2001, now abandoned the entire disclosure of which isincorporated herein by reference.

This application is based upon and claims the benefit of priority fromthe prior Japanese Patent Application No. 2000-102319, filed Apr. 4,2000, the entire contents of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

This invention relates to an image processing selecting method, an imageselecting method, and an image processing apparatus for processing aradiation image, and in particular, to an image processing selectingmethod, an image selecting method, and an image processing apparatuswhich are capable of selecting an optimum processing condition for aradiation image.

In recent years, an apparatus capable of radiographing a radiation imagedirectly as a digital image has been developed. For example, for anapparatus which detects the amount of radiation applied to a radiographyobject (subject) and obtains a radiation image formed corresponding tothe detected amount as an electrical signal, a number of methods inwhich a detector using a stimulable phosphor is employed have beendisclosed in the publications of unexamined patent applicationS55-12429, S63-189853, etc.

In such an apparatus, radiation which has once transmitted through aradiography object is applied to a detector having a stimulable phosphorlayer bonded on its sheet-shaped substrate by coating or vapordeposition, and is absorbed by the stimulable phosphor layer. Afterthat, by stimulating this stimulable phosphor layer by light or heatenergy, the radiation energy accumulated in this stimulable phosphorlayer through the above-mentioned absorption is emitted as a fluorescentlight, and this fluorescent light is photoelectrically converted, toobtain an image signal.

On the other hand, it has been proposed an apparatus for detectingradiation image which is obtained by generating charge corresponding tothe intensity of the applied radiation in a photoconductive layer,accumulating the generated charge in a plurality of capacitors arrayedtwo-dimensionally, and taking out the accumulated amounts of charge.

In such a radiation image detector, what is called a flat-panel detector(an FPD) is used. For an FPD of this kind, as described in thepublication of unexamined patent application H9-90048, it has been knownwhat is actualized by the combination of a phosphor emitting fluorescentlight in accordance with the intensity of the radiation applied and aphotoelectric conversion device such as a photodiode array or a CCDsensor which performs photoelectric conversion by receiving thefluorescent light emitted by the phosphor directly or through areduction optical system. Further, a similar FPD is noted in thepublication of unexamined patent application H6-342098.

In such an apparatus, in order to express a radiation image in agradation suitable for diagnosis, it is desirable to make a gradationconversion automatically for the image obtained by such an apparatus asmentioned in the above in a manner such that a medical doctor can easilyobserve the portion to be watched (interest region).

In order to carry out such an automatic gradation conversion, it is doneto determine the processing condition from the statistical feature ofthe image data (the maximum value, minimum value, histogram, etc. of thedata) to apply image processing to the whole image.

Further, in order to make the structure of minute portions easy toobserve, edge enhancement processing is carried out, and dynamic rangecompression processing for narrowing the signal range of the radiographyobject to make both of the portion of high density and the portion oflow density simultaneously easy to observe, etc. are also done.

However, in a radiography to be utilized in diagnosis, the body partbecoming the radiography target is diversified from the head to limbs,and because the region to be watched by a medical doctor is differentfor each case, the image processing condition for obtaining an imagewhich is most suitable for diagnosis becomes different for eachradiography body part. Further, in the same way, the processingcondition becomes different also in accordance with the radiographingorientation (the radiographing direction) in which the radiographyobject is placed.

For that reason, heretofore, in these apparatus, it is necessary toinput the radiographed body part of the radiography object, theradiographing orientation, etc. before carrying out image processing inorder to select the most suitable condition.

In some hospitals, there are provided a hospital information system(HIS) or a radiology information system (RIS), and the information onthe radiographed body part can be obtained directly from the orderinformation for the radiographing; therefore, without a specialoperation of a radiologist, the selection of the optimum processingcondition is possible; however, because in many hospitals, there isprovided no such system, it is necessary for a radiologist or some onelike that to input these bits of information manually.

Further, in a radiographing in an emergency, in order to carry out itrapidly, in some cases a radiologist or the other person manually inputsthe information on the body part of an radiography object etc. even inthe hospitals provided with the above-mentioned HIS or RIS.

However, there are more than one hundred kinds of body parts to begenerally radiographed, and it is troublesome to make theabove-mentioned manual input operation every time when radiographing iscarried out, which has been a burden for radiologists who carry outradiographing.

Therefore, in order to lighten the burden for radiologists, it has beenrequired to select the optimum processing condition easily for aradiographed image.

SUMMARY OF THE INVENTION

It is an object of this invention to actualize an image processingselecting method, an image selecting method, and an image processingapparatus capable of obtaining an optimum image for diagnosis without atroublesome operation, by selecting and presenting one or a plurality ofproper image processing conditions regarded as suitable ones throughrecognizing the region of a radiography object and the radiographingorientation, and selecting an optimum condition out of those imageprocessing conditions presented.

That is, this invention to solve the above-mentioned problem is asfollows.

(1) An image processing apparatus comprising radiation image formingmeans for detecting the radiation amount transmitted through aradiography object and forming a radiation image corresponding to thedetected amount, discriminating means for discriminating at least one ofthe body part of a radiography object and the radiographing orientation,for the radiation image formed by said radiation image forming means,image processing condition memorizing means for memorizing each of aplurality of image processing conditions in accordance with each of thebody parts of a radiography object, each of the directions ofradiographing, or each of the combinations of both of these, displaymeans for displaying a single or a plurality of image processingconditions, and image processing condition selecting means capable ofselecting an arbitrary image processing condition out of the imageprocessing conditions displayed on said display means, wherein saidimage processing condition selecting means reads out and displays one ora plurality of image processing conditions from said image processingcondition memorizing means on the basis of the result of discriminationobtained by said discriminating means, and accepts the selection of anarbitrary image processing condition out of said image processingconditions displayed.

Further, an image processing selecting method in an image processingapparatus comprising radiation image forming means for detecting theradiation amount transmitted through a radiography object and forming aradiation image corresponding to the detected amount, discriminatingmeans for discriminating at least one of the body part of a radiographyobject and the radiographing orientation for the radiation image formedby said radiation image forming means, image processing conditionmemorizing means for memorizing each of a plurality of image processingconditions in accordance with each of the body parts of a radiographyobject, each of the directions of radiographing, or each of thecombinations of both of these, display means for displaying a single ora plurality of image processing conditions, and image processingcondition selecting means capable of selecting an arbitrary imageprocessing condition out of the image processing conditions displayed onsaid display means, wherein one or a plurality of image processingconditions are read out from said image processing condition memorizingmeans on the basis of the result of discrimination obtained by saiddiscriminating means, the read out image processing conditions aredisplayed by said display means, and the selection of an arbitrary imageprocessing condition out of said image processing conditions displayedis accepted by said image processing condition selecting means.

According to these inventions, in processing a radiation image obtainedby detecting the radiation amount transmitted through a radiographyobject, image processing is carried out in a manner such that the bodypart of the radiography object and the radiographing orientation arediscriminated for the radiation image, one or a plurality of properimage processing conditions are automatically read out of plural imageprocessing conditions which have been optimized beforehand for each ofthe body parts of a radiography object and memorized, and one that isjudged as an optimum condition by an operator is selected on the basisof the read out image processing conditions.

Owing to this, for a radiation image, it becomes possible that one or aplurality of proper image processing conditions regarded as suitableones are selected and presented by recognizing the radiographed bodypart of a radiography object and the radiographing orientation, and anoptimum condition out of those presented image processing conditions isselected, which makes it possible to obtain an optimum image fordiagnosis without a troublesome operation.

(2) An image processing apparatus as set forth in (1), wherein saidimage processing condition selecting means comprises one or a pluralityof image display means, a processed image is produced by applying imageprocessing to a radiation image by the aforesaid image processing meansfor each of one or a plurality of image processing conditions read outfrom the aforesaid image processing condition memorizing means on thebasis of the result of discrimination by the aforesaid discriminatingmeans, and said processed images are displayed on said image displaymeans together with the image processing conditions applied to saidprocessed images respectively.

Further, an image processing selecting method as set forth in (1) in animage processing apparatus which is further provided with image displaymeans for displaying a radiation image having been subjected to imageprocessing by image processing means, wherein a processed image isproduced by applying image processing to a radiation image by theaforesaid image processing means for each of one or a plurality of imageprocessing conditions read out from the aforesaid image processingcondition memorizing means on the basis of the result of thediscrimination by the aforesaid discriminating means, and said processedimages are displayed on said image display means together with the imageprocessing conditions applied to said processed images respectively.

According to these inventions, in the above-mentioned (1), it isproduced a processed image which has been subjected to image processingfor each of one or a plurality of image processing conditions read outon the basis of the result of the discrimination, and said processedimages are displayed on said image display means together with the imageprocessing conditions applied to said processed images respectively.

Owing to this, in addition to the above-mentioned (1), by making theimage after image processing capable of being visually grasped, it ispossible to select an optimum image processing condition and a processedimage easily.

(3) An image processing apparatus as set forth in (1) or (2), whereinthe aforesaid image processing condition selecting means displays thename of image processing for specifying an image processing condition.

Further, an image processing selecting method as set forth in (1) or(2), the name of image processing for specifying an image processingcondition is displayed.

According to these inventions, in the above-mentioned (1) or (2), thename of image processing for specifying an image processing condition isdisplayed.

Owing to this, in addition to the above-mentioned (1) or (2), it becomespossible to grasp the kind of an image processing condition easily bythe display of the name of image processing, and an optimum imageprocessing condition can be easily selected.

(4) An image processing selecting method as set forth in (3), whereinthe aforesaid name of image processing is expressed by any one of thefollowing: a radiographed body part of a radiography object, aradiographed body part of a radiography object and the radiographingorientation, and the radiographing orientation.

Further, an image processing apparatus as set forth in (3), wherein theaforesaid name of image processing is expressed by any one of thefollowing: a radiographed body part of a radiography object, aradiographed body part of a radiography object and the radiographingorientation, and the radiographing orientation.

Owing to this, in addition to the above-mentioned (3), by making thename of image processing a name representing a radiographed body part ofa radiography object, a radiographed body part of a radiography objectand the radiographing orientation, or the radiographing orientation, itbecomes possible to grasp the kind of an image processing condition, andan optimum image processing condition can be more easily selected.

(5) An image processing selecting method as set forth in any one of (1)to (4), wherein, with respect to each of image processing conditions orthe image processing condition selected, it is also displayed whetherrotation of image is included in it or not or whether inversion of imageis included or not.

Further, an image processing apparatus as set forth in any one of (1) to(4), wherein, with respect to each of image processing conditions or animage processing condition selected, it is also displayed whetherrotation of image is included in it or not or whether inversion of imageis included or not.

According to these inventions, in the above-mentioned (1) to (4), withrespect to each of image processing conditions or an image processingcondition selected, it can be easily known whether inversion or rotationof image is included; therefore, the possibility of an erroneousdiagnosis or the like owing to a careless image processing can bereduced.

(6) An image processing apparatus comprising radiation image formingmeans for detecting the radiation amount transmitted through anradiography object and forming a radiation image corresponding to thedetected amount, discriminating means for discriminating at least one ofthe region of a radiography object and the direction of photographing,for the radiation image formed by said radiation image forming means,image processing condition memorizing means for memorizing each of aplurality of image processing conditions in accordance with each of theregions of a radiography object, each of the directions ofradiographing, or each of the combinations of both of these, imageprocessing means for applying image processing based on an arbitraryimage processing condition to said radiation image, and image selectingmeans displaying a single or a plurality of images and being capable ofselecting an arbitrary image out of the displayed images, wherein one ora plurality of suitable image processing conditions are read out fromsaid image processing condition memorizing means on the basis of theresult of discrimination obtained by said discriminating means, aprocessed image is produced by applying image processing to saidradiation image by said image processing means for each of one or aplurality of image processing conditions read out, one or a plurality ofsaid processed images are displayed on said image display meanssimultaneously or one by one by switching, and an arbitrary image can beselected out of said displayed processed images.

Further, an image selecting method in an image processing apparatuscomprising radiation image forming means for detecting the radiationamount transmitted through an photographic object and forming aradiation image corresponding to the detected amount, discriminatingmeans for discriminating at least one of the region of a radiographyobject or the radiographing orientation for the radiation image formedby said radiation image forming means, image processing conditionmemorizing means for memorizing a plurality of image processingconditions in accordance with each of the regions of a radiographyobject, each of the directions of radiographing, or each of thecombinations of both of these, image processing means for applying imageprocessing based on an arbitrary image processing condition to saidradiation image, and image selecting means displaying a single or aplurality of images and being capable of selecting an arbitrary imageout of the displayed images, wherein one or a plurality of suitableimage processing conditions are read out from said image processingcondition memorizing means on the basis of the result of thediscrimination obtained by said discriminating means, a processed imageis produced by applying image processing to a radiation image by theaforesaid image processing means for each of one or a plurality of imageprocessing conditions read out, one or a plurality of said processedimages are displayed on said image display means simultaneously or oneby one by switching, and an arbitrary image can be selected out of saiddisplayed processed images.

According to these inventions, one or a plurality of suitable imageprocessing conditions are read out on the basis of the result of thediscrimination, it is produced a processed image subjected to imageprocessing for each of the read out image processing conditions, one ora plurality of said processed images are displayed simultaneously or oneby one by switching, and an arbitrary image can be selected out of saiddisplayed processed images.

Owing to this, it is possible to select a processed image which has beenobtained by applying image processing to a radiation image in an optimumcondition, and the processed image can be visually grasped; therefore,it becomes possible to obtain an optimum image for diagnosis easily.

(7) An image processing apparatus as set forth in (6), wherein theaforesaid image selecting means displays the image processing conditionsapplied to the aforesaid processed images together with said processedimages.

Further, an image selecting method as set forth in (6), wherein theaforesaid image selecting means displays the image processing conditionsapplied to the aforesaid processed images together with said processedimages.

According to these inventions, in addition to the above-mentioned (6),by making the image after practicing image processing and the imageprocessing condition capable of being simultaneously grasped visually,the optimum image processing condition and processed image can be moreeasily selected.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram showing the structure of an imageprocessing apparatus of an example of the embodiment of this invention;

FIGS. 2( a) to 2(d) are illustrations showing how to extract a domain ofa radiography object in an example of the embodiment of this invention;

FIG. 3 is a flow chart of the external contour recognizing means of anexample of the embodiment of this invention;

FIG. 4 is an illustration of the detection of the border points of adomain of an example of the embodiment of this invention;

FIG. 5 is an illustration of the positional variation amount informationof an example of the embodiment of this invention;

FIG. 6( a) and FIG. 6( b) are illustrations for the example 1 of theexternal contour of the radiographed region of a radiography object ofan example of the embodiment of this invention;

FIG. 7( a) and FIG. 7( b) are illustrations for the example 2 of theexternal contour of the radiographed region of a radiography object ofan example of the embodiment of this invention;

FIG. 8( a) and FIG. 8( b) are illustrations for the example 3 of theexternal contour of the radiographed region of a radiography object ofan example of the embodiment of this invention;

FIG. 9 is a flow chart of the edge distribution recognizing means of anexample of the embodiment of this invention;

FIG. 10( a) to FIG. 10( d) are drawings showing the shape of a filterfor use in calculating the edge intensity information of an example ofthe embodiment of this invention;

FIG. 11( a) and FIG. 11( b) are illustrations for the example 1 of apattern of an edge distribution of an example of the embodiment of thisinvention;

FIG. 12( a) and FIG. 12( b) are illustrations for the example 2 of apattern of an edge distribution of an example of the embodiment of thisinvention;

FIG. 13 is a drawing showing how to select image processing in anexample of the embodiment of this invention;

FIG. 14 is a drawing showing how to select image processing in anexample of the embodiment of this invention; and

FIG. 15 is a drawing showing how to select image processing in anexample of the embodiment of this invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In the following, examples of the embodiment of this invention will beexplained by referring to the drawings.

In the following, the structure and the operation of an image processingapparatus will be explained for each of the blocks on the basis of roughblock-dividing.

As shown in FIG. 1, an image which is radiographed by the radiationtransmitted through a radiography object by the radiation image formingmeans 10 is transmitted to the reduced image generating means 20. In thereduced image generating means 20, in order to make high the speed ofthe successive processes, a thinned image (reduced image) having itsnumber of pixels reduced as compared to said radiation image isproduced, and is transmitted to the discriminating means 30. In thediscriminating means 30, the radiographed region of the object, theradiographing orientation, or the both of them are recognized byreferring to this thinned-out image, and one or a plurality of pieces ofthe region information indicating regions considered as suitable onesrespectively are transmitted to the image processing selecting means 40.In the image processing selecting means 40, the image processingconditions based on the obtained region information are read out fromthe image processing condition memorizing means 50, and presented to theuser. Further, the image processing condition selected by the user outof the presented image processing conditions is transmitted to the imageprocessing means 60, where processing for said radiation image iscarried out on the basis of the obtained image processing condition, andan image which has been finally processed is outputted.

In addition, each of means in the image processing apparatus of thisexample of the embodiment can be composed of a hardware, a firmware, ora software. Therefore, a functional block diagram following theprocedure of processing in each of the means is shown.

{1} Generation of Radiation Image:

As shown in FIG. 1, an image having signal values which areproportionate to the logarithm of the radiation amount applied isgenerated by the radiation image generating means.

For this radiation image generating means 10, such one that uses asensor or the like such as the above-mentioned FPD or CCD, or an alreadyknown apparatus which generates a radiation image through reading astimulable phosphor plate can be used. In addition, it is assumed that,in any case in this example of practice, signal values which areproportionate to the logarithm of the radiation amount applied areobtained, and the more the applied radiation amount is, the higher thesignal value is.

Further, in order to shorten the time required for the processes afterthis, it is produced a thinned-out radiation image having its number ofpixels reduced by sampling from the original radiation image by thereduced image generating means 20, and this thinned-out radiation imageis transmitted to the discriminating means 30. In the case where theprocessing in the image processing apparatus is done at a high speedenough, or in the case where it is of no problem for the processing totake a long processing time, it is possible to transmit a radiationimage which has not been subjected to thinning.

In addition, in the explanation of this example of the embodiment, it isassumed that the successive processes after this is carried out using athinned-out radiation image.

It is desirable that the thinned-out radiation image has as small anumber of pixels as possible, because the calculation time is shortenedin the various kinds of processes. However, in this example of theembodiment, it is necessary that such an amount of information as to beable to discriminate the feature of a radiography object is provided.Therefore, in the case where a radiation image of original size has beenobtained for each of regions of a human body, it is desirable that thepixel is made to have a size from 1 mm square to 5 mm square or so.

{2} Discrimination:

In the discriminating means 30, first, the radiation image which hasbeen transmitted from the reduced image generating means 20 is analyzed.By doing this, the radiographed region (radiographed body part) of aradiography object and the radiographing orientation are discriminated.Further, as shown in FIG. 1, this discriminating means 30 contains in itthe feature vector generating means 310 (the radiographed object regionextracting means 311 and the feature extracting means 312), thecorrelativity calculating means 320, the correlation result comparingmeans 330 (the temporary memory means 340 included), and the radiographyobject information memorizing means 360.

First, in the feature vector generating means 310, the radiographedobject region is extracted, and by using the label information of theextracted radiographed object region, a feature vector having aplurality of elements is generated and is sent to the correlativitycalculating means 320.

In the correlativity calculating means 320, when a feature vector fromthe feature extracting means 312 is received, object vectors which havebeen memorized beforehand in the radiography object informationmemorizing means 320 are successively drawn out and the correlationoperation with the feature vector is carried out. Further, each of thecorrelation values obtained from the result of the correlation operationby this correlativity calculating means 320 is transmitted to thecorrelation result comparing means 330.

In the correlation result comparing means 330, it is carried out thecomparison between a threshold value which has been determinedbeforehand and each of the transmitted correlation values. If acorrelation value is not smaller than said threshold value, the regioninformation (the body part information) corresponding to the objectvector concerned is memorized in the temporary memory means 340.

After the correlation operation with all of the object vectors has beenfinished, the region information memorized in the temporary memory means340 is read out and transmitted to the image processing conditionselecting means 40. If there is no region information memorized in theabove-mentioned temporary memory means 340, particular regioninformation which has been specified beforehand is transmitted to theimage processing condition selecting means 40.

{2-1} Generation of a Feature Vector

First, the radiographed object region is extracted in the radiographedobject region extracting means 311. Then, the label informationindicating the extracted radiographed object region and the thinned-outradiation image are transmitted to the feature extracting means 312.

In the feature extracting means 312, a feature vector having a pluralityof elements is generated by using the label information of the extractedradiographed object region. In addition, regarding a feature vector, anexplanation will be given later. Then, the feature extracting means 312transmits the obtained feature vector to the correlativity calculatingmeans 320.

{2-1-1} Extraction of a Radiographed Object Region:

Now, the radiographed object region extracting means 311 carries out theextraction of the radiographed object region as will be explained below(refer to FIG. 2).

{2-1-1-a} The image is divided into plural small regions (FIG. 2( a)).

{2-1-1-b} In each of the small regions, an average signal value of thepixel signal values included in said small region is obtained as athreshold value Th1.

{2-1-1-c} For each of the small regions, pixels having a signal valuelower than the threshold value Th1 are detected as the radiographedobject region (FIG. 2( b)).

{2-1-1-d} The average signal value of the radiographed object regionobtained in each small region is obtained and is made the thresholdvalue Th2.

{2-1-1-e} Over the whole image, pixels having a signal value lower thanthe threshold value Th2 are detected as the radiographed object region(FIG. 2( c)).

{2-1-1-f} In order to remove the outside region of an irradiation fieldfrom the detected radiographed object region, the border lines of theoutside region of the irradiation field are obtained, and the portionbetween the border lines and the nearer image edges is removed as anoutside region of the irradiation field (FIG. 2( d)).

{2-1-1-g} The border lines of the outside region of the irradiationfield are obtained in the following way. First, pixels positioned at theborder of the radiographed object region are detected as border points.Then, a straight line on which a number of border points in the samedirection are arrayed is detected as a line considered as suitable onefor the border lines. With respect to the line considered as suitableone for the border lines, an equation of a straight line is calculatedfrom two arbitrary border points, and if the number of border pointsexisting on the straight line is not smaller than a specified thresholdvalue Th3, it is detected as the suitable one for the border lines.Further, in the case where the portion between the suitable line for theborder and the image edge is almost the radiographed object region, thesuitable line for the border is regarded as one of the border lines ofthe outside region of the irradiation field and the radiographed objectregion from it to the image edge is eliminated as an outside region ofthe irradiation field.

The radiographed object region information indicating the radiographedobject region which has been obtained by the respective means(respective steps) of the above-mentioned {2-1-1-a} to {2-1-1-g} isgiven as a domain indicating image having the same size as thethinned-out image having been obtained by the reduced image formingmeans 20 (the original image in the case where the image obtained fromthe radiation image forming means 10 is used directly), in which pixelsoutside the radiographed object region are set at the pixel value ‘0’,pixels included in the radiographed object region are set at the pixelvalue ‘1’, and pixels positioned on the above-mentioned border lines ofthe outside region of the irradiation field (edges of the irradiationfield) are set at the pixel value ‘2’.

Further, as a method of carrying out the extraction of a radiographedobject region by the radiographed object region extracting means 311, inaddition to the above-mentioned procedure explained in {2-1-1-a} to{2-1-1-g}, a method to be shown in the following {2-1-1-h} and {2-1-1-i}can be considered.

{2-1-1-h} After the outside region of the irradiation field is detectedby the method which is described in the publications of the unexaminedpatent application S63-259538, S63-244029, and H5-7579, signal valuescorresponding to the domain which has been directly irradiated byradiation are found out from the shape of the histogram of the pixelsignal values in the irradiation field domain, and the residualirradiation field domain after the domain corresponding to theabove-mentioned signal values is removed is made the radiographed objectregion. With respect to the detection of the above-mentioned signalvalues corresponding to the directly irradiated domain by radiation, itis made possible by the following procedure: that is, by means such asdiscrimination analysis for example, a threshold value between a highsignal domain indicating the directly irradiated domain and a low signaldomain having a lower signal than that owing to the transmission througha radiography object is obtained in the above-mentioned histogram, and adomain having a higher signal than said threshold value is regarded asthe directly irradiated domain.

{2-1-1-i} Further, in order to avoid the influence by the heel effect,an unevenness caused by the radiation image forming means, etc., for thedetection of the threshold value for removing the above-mentioneddirectly irradiated region, it is possible to use a method, in which itis produced a histogram of pixel signal values for each of the pluralblock-wise domains formed, for example, by dividing an image into fourportions through dividing into the upper and lower portions, anddividing into left and right portions, and means such as discriminationanalysis is used as described in the above.

The radiographed object region information indicating the radiographedobject region which has been obtained by the means of theabove-mentioned {2-1-1-h} or {2-1-1-i} is given, in the same way as thecase of it being obtained by the means shown in the procedure {2-1-1-a}to {2-1-1-g}, as a domain indicating image having the same size as thethinned-out image which has been obtained by the reduced image formingmeans 20 (the original image, in the case where the image obtained fromthe radiation image forming means 10 is used directly), in which pixelsoutside the radiographed object region are set at the pixel value ‘0’,pixels included in the radiographed object region are set at the pixelvalue ‘1’, and pixels positioned on the above-mentioned border lines ofthe outside region of the irradiation field (edges of the irradiationfield) are set at the pixel value ‘2’.

In the case where the radiographed object region is composed of aplurality of domains which are not connected to one another, only thelargest domain among them is extracted. With respect to the calculationof the number of the radiographed object regions and the classificationof the domains, for example, labeling processing which has beenheretofore often used can be utilized. In the case where theradiographed object region is classified into a plurality of domains inthis way, the numbers of pixels included in the respective domains arecounted, and only the domain having the largest number of pixels is madethe radiographed object region anew, while the radiographed objectregion information is renewed.

In this way, the radiographed object region information obtained by therespective steps of the above-mentioned {2-1-1-a} to {2-1-1-g},{2-1-1-h}, or {2-1-1-i} in the radiographed object region extractingmeans 311 is transmitted to the feature extracting means 312 togetherwith the above-mentioned thinned-out image.

{2-1-2} Feature Extraction:

In the feature extracting means 312, a plurality of features areextracted mainly from the radiographed object region, and each of themis denoted by the element of the feature vector Cj (j=1, 2, - - - , m).For the features to be extracted, the size of the radiographed objectregion, the shape, the shape of the density profile, the shape of thecenter line of the radiographed object region, the distribution of edgesbased on the first order derivative, or second order derivative derivedfrom the neighborhood pixels, strength or weakness of the variance valueof the signal values in each of the local regions, etc. can be cited.

The value of each element Cj is memorized as a vector value on the basisof a predetermined condition. For example, assuming that the element Csof the feature vector denotes the “external contour (outline) of aradiography object”, and it is classified into any one of the threetypes of “rectangle type”, “barrel type”, and “sandglass type”, Cs ismade a vector having the three elements (e0, e1, e2). Each of theelements ek (k=0, 1, 2) is made to correspond to each of the “rectangletype”, “barrel type”, and “sandglass type”. Then, if the shape is judgedas nearly rectangular like a forearms or a femur, Cs is expressed byCs=(1, 0, 0), and if it is judged as nearly cask-shaped, Cs is expressedin such a manner as Cs=(0, 1, 0).

In this example of practice, explanations will be given by assuming thatthe features to be used are “the external contour of the radiographedobject region” and “the spatial distribution of edges”, and the featurevector P has the elemental vectors C0 and C1 based on those features. Inthe following, the external contour recognizing means 100 (refer to FIG.3) and the edge distribution recognizing means 200 (refer to FIG. 9)will be explained.

{2-1-3} Recognition of External Contour:

In the external contour recognizing means 100 for carrying out therecognition of an external contour, by using the variation of thecontour of the radiographed object region and the information on thewidth of the radiographed object region, the external contour of theradiographed object region is recognized, and the radiographed objectregion is classified into several types depending on the externalcontour recognized. The result of classification is outputted as afeature amount (features).

First, the radiographed object region information is inputted in theregion border point detecting means 110, and a plurality of regionborder points expressing the contour of the radiographed object regionare obtained. The obtained region border points are transmitted to theposition variation calculating means 120 for calculating the localvariation of position of the contour, and the region width calculatingmeans 130 for obtaining the width of the radiographed object region. Inthe position variation calculating means 120, the size and position ofthe concavity or convexity of the contour are obtained, and in theregion width calculating means 130, the local width of the radiographedobject region is obtained along a plurality of scan lines drawn acrossthe radiographed object region. These kinds of information aretransmitted to the contour specifying means 140, where the externalcontour of the radiographed object region is specified on the basis ofthese bits of information. Then, the feature amount is outputted on thebasis of the specified external contour.

{2-1-31} Detection of Region Border Points:

The region border point detecting means 120 carries out followingprocessing. As shown in FIG. 4, first, for a region indicting imagewhich indicates the radiographed object region information, a pluralityof different scan lines scanning the image sequentially from one edge tothe other edge in the horizontal direction at equal intervals are set.

Further, on each of the scan lines, pixel values are checkedsuccessively one by one as moving from the left edge of the image to theright side, and the pixel at a position where the pixel value variesfrom ‘0’ to ‘1’ or from ‘2’ to ‘1’ is detected as a region border point(left). After that, pixel values are checked on the same scan linesuccessively one by one as moving from the right edge to the left sidethis time, and the pixel at a position where the pixel value varies from‘0’ to ‘1’ or from ‘2’ to ‘1’ is also detected as a region border point(right). If the pixel value at an image edge is ‘1’, the pixel at theimage edge on the scan line is made a region border point. For each ofthe detected region border points, the coordinate values and theinformation indicating which the border point belongs to, (left) or(right), are transmitted to the position variation calculating means 120and the region width calculating means 130.

{2-1-3-2} Calculation of Amount of Position Variation:

With respect to the region border points obtained by the above-mentionedregion border point detecting means 110, the position variationcalculating means 120 calculates the difference of the horizontalcoordinate value between the neighboring region border points iscalculated for each of the (left) and (right) groups.

Next, from the above-mentioned difference of the horizontal coordinatevalue, with respect to the horizontal coordinate for each of theabove-mentioned groups, the maximum point where the shape of theradiographed object region becomes ‘convex’ (in the case of (left)group, it corresponds to the point where the horizontal coordinatelocally comes to the extremely left side, and in the case of (right)group, it corresponds to the point where the horizontal coordinatelocally comes to the extremely right side) and the minimum point wherethe shape of the radiographed object region becomes ‘concave’ (in thecase of (left) group, it corresponds to the point where the horizontalcoordinate locally comes to the extremely right side, and in the case of(right) group, it corresponds to the point where the horizontalcoordinate locally comes to the extremely left side) are obtained.

Further, regarding these extreme points (the maximum point and theminimum point), the degree of concavity or convexity in the neighborhoodof them is studied. The position of the extreme point and the degree ofconcavity or convexity are calculated in the following way.

The explanation given below can be applied to the (left) group and the(right) group similarly; therefore, explanation will be given only forone of the groups.

{2-1-3-2a} Detection of the Position of an Extreme Point:

With respect to the region border points other than those existing atthe uppermost or the lowermost portion of the radiographed objectregion, the following processes are carried out successively from theupper side of the radiographed object region.

The difference value of the horizontal coordinate s1 between the regionborder point concerned (the region border point other than thoseexisting at the uppermost or the lowermost portion of the radiographedobject region) p0 and the neighboring region border point p1 existing atthe upper side of p0 is obtained. In the same way, the difference valueof the horizontal coordinate s2 between the region border point p0concerned and the neighboring region border point p2 existing at thelower side of p0 is obtained.

Next, the sign of s1×s2 is checked, and if it satisfies a specifiedcondition, the extreme point is detected.

If s1×s2<0, said region border point p0 is regarded as an extreme point.

If s1×s2=0 and only one of sj (i=1, 2) is ‘0’, the difference values ofhorizontal coordinate between p0 and the object border points existingin the neighborhood of it are calculated successively in the order fromthe nearest to the farther point to the direction in which thedifference value is ‘0’ (upper or lower direction). Then, when thedifference value takes a value other than ‘0’ for the first time, thedifference value is made sj anew. Further, s1×s2 is again calculated. Atthis time, if s1×s2<0, the middle point between the above-mentioned p0and the region border point where sj takes a value other than ‘0’ forthe first time is made an extreme point.

{2-1-3-2b} Degree of Concavity or Convexity:

Now, the degree of concavity or convexity will be explained by referringto FIG. 5. First, the difference value of the horizontal coordinatebetween neighboring region border points are successively checked fromthe extreme point to the upper side, and a point “a” where thedifference value becomes of reverse sign to the difference value in theupper neighborhood of the extreme point or ‘0’ is obtained. Further, inthe same way, the difference value of the horizontal coordinate betweenneighboring region border points are successively checked from theextreme point to the lower side, and a point “b” where the differencevalue becomes of reverse sign to the difference value in the lowerneighborhood of the extreme point or ‘0’ is obtained. Regarding suchpoints “a” or “b”, if a point where the difference value becomes ofreverse sign to that in the neighborhood of the extreme point cannot befound, the points having the uppermost vertical coordinate and thelowermost vertical coordinate in the area where the object exists aremade the point “a” and the point “b” respectively. The differencebetween the average value of the horizontal coordinate of these points“a” and “b” and the horizontal coordinate value of the extreme pointconcerned is regarded as the depth (refer to FIG. 5), and the differenceof the vertical coordinate value between the points “a” and “b” isregarded as the width (refer to FIG. 5), and each of them is made anindex expressing the degree of concavity or convexity. Further, as amethod of obtaining the above-mentioned points “a” and “b”, instead ofusing the difference value as the basis, it is appropriate to use thesecond order derivative value of the horizontal coordinate as the basis.Here, also in the case where the second order derivative value is usedas the basis, a point where the second order derivative value becomes ofreverse sign to that in the neighborhood of the extreme point or ‘0’ ismade the point “a” or the point “b”.

In studying this degree of concavity or convexity, it is desirable to dosuch a contrivance as to enlarge the distance between the region borderpoints for calculating the above-mentioned difference value to someextent so as not to make an error in detecting the variation on thewhole by detecting minute variations. For example, there is such amethod as to use only the region border points positioned on the linesto divide the length of the radiographed object region in the verticaldirection (the direction perpendicular to the scan lines in detectingthe region border points) into 10 to 30 equal portions, or as to obtainan average horizontal coordinate value of plural neighboring regionborder points, to obtain the above-mentioned difference value on thebasis of the average horizontal coordinate value.

Further, at a position where the radiographed object region meets theedge of the irradiation field, it sometimes occurs that the radiographedobject region is made to have a convex shape, which is different fromthe original shape of the object (refer to the enlarged drawing in FIG.5). Therefore, in the case where the extreme point is a maximum pointand the radiographed object region meets the edge of the irradiationfield, this extreme point is to be regarded as not detected, and itsdepth and width are both made ‘0’.

In the above-mentioned cases, the judgment whether the radiographedobject region meets the edge of the irradiation field or not is done inthe following way. That is, at a plurality of region border points whichare located in either the upper or lower neighborhood of an extremepoints, if there is a pixel having the pixel value ‘2’ of the domainindicating image, which indicates an edge of the irradiation field,within a range of a specified distance (from one pixel to three pixelsor so), it is judged that the radiographed object region meets an edgeof the irradiation field. Moreover, in the case where the radiographedobject region meets an edge of the image, the point is treated in thesame way.

Among the extreme points determined in the above-mentioned manner, onethat has a large degree of concavity or convexity is generally regardedas one of points representing the external contour. Therefore, for eachof the above-mentioned (left) and (right) groups, only a specifiednumber of points (desirably 1 to 3) are extracted in the order of theabsolute value of the depth in which the largest one comes first, a setof information on the position of the extreme point, concavity orconvexity, the depth, and the width is made the positional variationamount information.

Further, in the same way as the above-mentioned positional variation ofthe object in the horizontal direction, extreme points are obtained forthe positional variation in the vertical direction, and both sets ofpositional variation amount information are transmitted to the contourspecifying means 140.

{2-1-3-3} Calculation of the Region Width:

In the region width calculating means 130, it is obtained the distancebetween the border points which are located on the same scan line amongthe above-mentioned region border points. The obtained distances,together with the coordinate values of the scan line in the verticaldirection, are transmitted as the region width information to thecontour specifying means 140.

{2-1-3-4} Contour Specifying:

In the contour specifying means 140, the external contour is specifiedfrom the positional variation amount information and the region widthinformation obtained, by classifying it into a plurality of patternsprepared beforehand. The external contour of a human body in radiographyhas a feature which is different depending on the region to be theradiography object. To take the head image as shown in FIGS. 6( a) and6(b) for instance, when the sets of the positional variation amountinformation obtained from the (left) and (right) groups respectively arestudied from the upper edge of the image to the lower edge side, bothmake variation such that the border points come closer to the left edgeand right edge of the image midway respectively, and later, both makevariation such that the border points go away from the image edges;therefore, the external contour can be classified as “barrel type”.Further, in the neck image as shown in FIGS. 6( c) and (d), it is foundout that, on the contrary, both border lines make variation such thatthe border points go away from the image edges midway respectively, andlater both come closer to the image edges respectively. In such a caseas this, the external contour can be classified as “sandglass type”.

Further, with respect to the abdomen image as shown in FIGS. 7( a) and7(b), and the lower limbs image as shown in FIGS. 7( c) and 7(d), thepositional variation of the region border point in the horizontaldirection is not so much, and both have approximately rectangularexternal contour; however, by using the region width information, bothcan be classified as “square type” and “rectangle type” respectively,because the abdomen image has a broad width, while the lower limbs imagehas a narrow width.

Further, in the knee joint image as shown in FIGS. 8( a) and 8(b),because the joint portion has a shape having a crook midway, it can beclassified as “boomerang type”. A classification including, in additionto the above-mentioned, “fan type” for the shoulder joint, “ramificationtype” for the hand fingers, “single side-convex type” for the heel,“reverse triangle type” for the clavicle, etc. can be thought of.

{2-1-3-4a} Detailed Explanation of the Example of Classification:

By using the above-mentioned positional variation amount information andthe region width information, the external contour of a radiographyobject is classified as a shape considered most suitable among theseveral patterns shown in the above.

{2-1-3-4a(1)} Condition of Classification as Barrel Type:

For example, the condition for classifying a shape as “barrel type” isdefined in the following way.

-   -   The broadest extreme point is convex for each of the (left) and        (right) groups;    -   The position of both of the above-mentioned extreme points is in        the central part of the three parts formed by dividing the        radiography object into three parts in the vertical direction;    -   The depth of each of the above-mentioned two extreme points is        not less than 20% of the average width of the object in the        horizontal direction.

{2-1-3-4a(2)} Condition of Classification as Sandglass Type:

In the same way, the condition for classifying a shape as “sandglasstype” is defined in the following way.

-   -   The broadest extreme point is concave for each of the (left) and        (right) groups;    -   The position of both of the above-mentioned extreme points is in        the central part of the three parts formed by dividing the        radiography object into three parts in the vertical direction;    -   The sum of the depth of each of the above-mentioned two extreme        points is not less than 20% of the average width of the object        in the horizontal direction.

{2-1-3-4a(3)} Condition of Classification as Boomerang Type:

-   -   The broadest extreme point is convex for one and concave for the        other of the (left) and (right) groups;    -   The depth of each of the above-mentioned two extreme points is        not less than 10% of the average width of the object in the        horizontal direction.

{2-1-3-4a(4)} Condition of Classification as Squar Type, Rectangle Type:

Further, the condition for classifying a shape as “square type” or“rectangle type” is defined in the following way.

-   -   The depth of the extreme point having the largest depth is less        than 10% of the average width of the object in the horizontal        direction for each of the (left) and (right) groups.    -   If the ratio of the length of the object in the horizontal        direction to the length in the vertical direction is less than 2        with the shorter one taken as the basis, it is classified as        “square type”;    -   If the ratio of the length of the object in the horizontal        direction to the length in the vertical direction is not less        than 2 with the shorter one taken as the basis, it is classified        as “rectangle type”.

In addition, each of the conditions of classification shown in the aboveconcrete examples is only one example, and some other kinds ofconditions can be considered.

{2-1-3-4b}

As explained in the above, the external contour is classified intoseveral patterns by the positional variation amount information and theregion width information. The result of the classification is outputtedas the feature amount which is finally obtained. The result of thisoutput is memorized as associated with the each element of the elementalvector C0 of the above-mentioned feature vector P. The shape vector S isexpressed by it that only the element corresponding to the result of theabove-mentioned classification takes a value other than ‘0’. Forexample, the number of the element corresponding to each of “barreltype”, “sandglass type”, “boomerang type”, “square type”, “rectangletype”, and “other”, is set at ‘0’, ‘1’, ‘2’, ‘3’, ‘4’, and ‘5’. Then, ifthe result of the classification is “barrel type”, C0[0]=1 is put, andif it is “sandglass type”, C0[1]=1 is put; both are memorized. In thisway, this elemental vector C0 is outputted as the feature amount fromthe contour specifying means 140.

{2-1-3-4c} Basis of the Discrimination of Classification:

Further, in some cases it is difficult to classify the shape simply as acertain type among them.

Therefore, it is appropriate to carry out weighting on the featureamount corresponding to each of the patterns that can be somewhatsuitable for the shape, to output the result. In this case, setting isdone in a manner such that a plurality of elements of theabove-mentioned elemental vector C0 is made to have a value other than‘0’, and the sum of the values of the elements becomes a constant value(‘5’ for example). Besides, the values are allotted in such a way thatan element corresponding to a shape of the higher degree of certaintyhas the larger value.

For example, in the case where the condition for the depth is notsatisfied by only a little amount in the above-mentioned basis of thediscrimination for the “sandglass type”, that is, the sum of the depthvalues of the extreme points is only 18%, for example, of the averagewidth of the object in the horizontal direction, it is delicate that theshape should be classified as “square type” or “sandglass type”. In sucha case as this, values are allotted to the element of the elementalvector C0 representing the “sandglass type” and the element representingthe “square (rectangle) type”.

In this case, as an example of allotment, if the sum of the depth valuesis not larger than 10%, it is set that the element representing the“square type” C0[3(4)]=5, the element representing the “sandglass type”C0[1]=0, and for every increment of the above-mentioned sum of the depthvalue by 2%, the value of C0[3(4)] is reduced by ‘1’, and on thecontrary, the value of C0[1] is increased by ‘1’.

Also in the cases other than this example of the “sandglass type” and“square (rectangle) type”, that is, between the “barrel type” and“square (rectangle) type”, the “square type” and “rectangle type”, the“boomerang type” and “square (rectangle) type”, etc., a basis of thediscrimination of classification similar to the one shown in the abovecan be applied.

{2-1-4} Recognition of Edge Distribution:

As shown in FIG. 9, the following processing of the recognition of edgedistribution is carried out by the edge distribution recognizing means200.

{2-1-4-1} Extraction of Signal Variation:

The signal variation extracting means 210 carries out the extraction ofsignal variation (for example, the extraction of an edge in a boneregion) as will be explained in the following.

In this example of the embodiment, in order to extract an edge of a boneregion or the like especially, an operation process equivalent to secondorder differential is practiced.

An operation process is practiced for the density of every pixel P(x, y)(x: horizontal coordinate value, y: vertical coordinate value) of theabove-mentioned thinned-out image using four kinds of filters as shownin FIGS. 10( a) to 10(d).

Now, the edge intensity information Q(x, y) obtained as the result ofthe operation of every pixel P(x, y) is expressed by the followingequation:

[M1]Q(x, y)=a-max(ΣΣP(x+i, y+j)Mn _(ij)),where Mn_(ij) represents each of the values in each filter (n=1, 2, 3,4).

Further, a-max( ) indicates the maximum value to be obtained bycomparing the absolute value of the result of the operation using theabove-mentioned filters.

Moreover, the value of Mn_(ij) for each pixel is the value noted in eachsquare of the filters shown in FIG. 10.

Further, in practicing the above-mentioned operation at an end portionof an image, in the case where a pixel whose density is to be multipliedby the filter value is located out of the image area and does not exist,the operation process is carried out through virtually substituting thedensity value of the central pixel of the filter operation for thedensity value of the pixel that comes out of the image area.

Further, the edge direction information D(x, y) is given byD(x, y)=n,where n is corresponding to the number of the filter which has beenselected by the above-mentioned operation process for a-max( ), and itis memorized for each of pixels. The edge direction information D(x, y)becomes an index indicating the direction of the edge at the pixel. Forexample, when n=1, the direction of the edge is horizontal, and whenn=2, the direction of the edge is vertical.

At pixels having important information such as an edge in a bone region,signal variation between neighboring pixels is large. Therefore, byextracting the pixels having a high edge intensity from theabove-mentioned edge intensity information Q(x, y), important edgeinformation can be obtained.

Therefore, the values of the above-mentioned Q(x, y) are calculated overthe whole image, and it is discriminated if the value of Q(x, y) fallswithin the range of α% of the whole arranged in the order of the valueof Q(x, y), in which the largest one comes first, and the result isstored in the edge selection information L(x, y), where L(x, y) isexpressed by the following values:L(x, y)=1,(in the case where the value of Q(x, y) falls within the range of α% ofthe whole arranged in the order of the value of Q(x, y), in which thelargest one comes first)L(x, y)=0.(in the case where the value of Q(x, y) does not fall within the rangeof α% of the whole arranged in the order of the value of Q(x, y), inwhich the largest one comes first)

It is desirable that the above-mentioned α% is from 5% to 40% in orderto obtain necessary edge information correctly.

Further, in order to obtain specified edge information with enhancementput on it, it is appropriate to refer to the density value of each pixelitself. For example, because a bone part absorbs more amount ofradiation than a soft part, its density value becomes relatively low.Therefore, if the above-mentioned L(x, y)=1 only for pixels whose P(x,y) is lower than a specified value such as an average value of the wholeimage or an radiographed object region obtained by a method to bedescribed later, the edge corresponding to the border of a bone part canbe detected preferentially.

On the contrary, if the above-mentioned L(x, y)=1 only for pixels whoseP(x, y) is larger than the above-mentioned specified value, the signalvariation in the lung field, the border between a radiography object andthe directly irradiated region which is irradiated directly byradiation, etc. can be detected preferentially. Each of the edgedirection information D(x, y), the edge selection information L(x, y),and the edge intensity information Q(x, y) obtained in this signalvariation extracting means 210 is transmitted to the pattern detectingmeans 220.

{2-1-4-2} Pattern Detection:

In the pattern detecting means 220, one or a plurality of patterns aredetected from the edge direction information D(x, y), the edge selectioninformation L(x, y), and the edge intensity information Q(x, y)obtained.

When radiography is made with a human body put as a radiography object,the radiograph has a feature pattern depending on the region to becomethe object and the radiographing orientation. To take it for instancethe case where bones of limbs such as the image of the leg region shownin FIGS. 11( a) and 11(b) are made the radiography object, an edge (theportion shown with enhancement by the heavy line in FIGS. 11( a) and11(b)) having a relatively high intensity and a component in thedirection perpendicular to the border line exists continuously along theborder line of a bone. Such a continuous edge in the same directionappears in not only the bones of limbs but also in the contour of thelung field, the contour of a jawbone (FIG. 12( b)), etc. likewise.

Further, as the image of the lumbar shown in FIG. 12( a), in the casewhere the spine is the main radiography object, because the spine iscomposed of small bones assembled, edges having a high intensity areconcentrated in the spine part, but the directions of those edges arenot unified.

Therefore, as shown in the following, by carrying out the extraction of“patterns” such as “a continuous edge having the same direction”(appearing in the bones of limbs, contour of the lung field, etc.), and“a line of concentrated edges without directivity” (appearing in thespine part), information that is useful for recognizing the region ofthe radiography object can be obtained.

{2-1-4-2a} Pattern 1: Extraction of a Continuous Edge Having the SameDirection:

{2-1-4-2a-1} In the target pixel I(x, y) for which L(x, y)=1, if theboth adjacent pixels I(i, j) and I(k, l) that are adjacent to it in thedirection perpendicular to the edge direction indicated by D(x, y) havean edge component in the same direction and L(i, j)=1, L(k, l)=1 in thesame manner as I(x, y), the value of L(x, y) is increased by ‘1’.

{2-1-4-2a-2} Next, with respect to the target pixel J(x, y) for whichL(x, y)=2, if the both pixels J(i, j) and J(k, l) that are adjacent toit in the direction perpendicular to the edge direction indicated byD(x, y) have an edge component in the same direction and L(i, j)=2, L(k,l)=2 in the same manner as J(x, y), the value of L(x, y) is furtherincreased by ‘1’.

{2-1-4-2a-3} Further, the processing of the above-mentioned {2-1-4-2a-2}is further repeated m times. Then, if edges not less than (m+2) havingthe same direction and an intensity of not less than a certain value arecontinuously exists, in the central pixel of the continuous edges,L(x, y)=m+1is obtained. Therefore, by comparing the threshold value Thd1 with thevalue of L(x, y) of an arbitrary pixel, whenL(x, y)>Thd 1   (1)is satisfied by the L(x, y), a pattern representing “a continuous edgehaving the same direction” is extracted.

In the above, it is desirable that the threshold value Thd1 has a valuecorresponding to about 5 to 20 cm in the actual size of the radiographyobject.

Further, in the above-mentioned case, in order to shorten thecalculation time, it is also appropriate to adopt a method shown belowinstead of the method shown in {2-1-4-2a-1} to {2-1-4-2a-3}.

{2-1-4-2a-4} An image is divided into a plurality of small domains.

{2-1-4-2a-5} In each of the small domains, for the pixels included inthe small domain concerned, the number of pixels for which L(x, y)=1 iscounted in every edge direction indicated by D(x, Y), and is memorizedin En (n=1, 2, 3, 4), where n is the same as the index of the edgedirection owned by the above-mentioned D(x, y).

{2-1-4-2a-6} On the basis of the result of counting in theabove-mentioned {2-1-4-2a-5}, with respect to an arbitrary small domain,if the number of the edge components Em of the Em (m=1, 2, 3, 4) in acertain direction takes the majority of them and has a value not smallerthan a predetermined value, it is regarded as true that a patternrepresenting “a continuous edge having the same direction” is includedin the small region.

{2-1-4-2a-7} Further, it is also appropriate that, by uniting the methoddescribed in the above-mentioned {2-1-4-2a-1} to {2-1-4-2a-3} and thatdescribed in the {2-1-4-2a-4to {2-1-4-2a-6}, a pattern representing “acontinuous edge having the same direction” is included in a small domainwhere a number of pixels for which the condition L(x, y)>a (a: anarbitrary number larger than 0) is satisfied, which has been obtained in{2-1-4-2a-1} to {2-1-4-2a-3}.

In the case where two patterns extracted in the above exist close toeach other, they are regarded as patterns detected along the both sidesof a long big bone such as the femur. In such a case, it is consideredthat a feature of “a long bone” is detected, and the number of times ofdetecting such a pattern is inputted in the element A[0] of the elementA[u] (u=0, 1, - - - )

For the judgment if the two patterns are close to each other, thedistance between the patterns are checked along the edges of the patternrepresenting “a continuous edge having the same direction”, and if thedistance is not larger than a specified value (for example, the numberof pixels corresponding to 3 cm in the actual size of the radiographyobject), they are judged as close to each other.

Further, in the case where the above-mentioned “continuous edge havingthe same direction” exists close to each of the both left and right endsof the radiographed object region, it is high the possibility of thecontour of the lung field having been detected; therefore, in such acase, it is considered that the feature “the lung field” has beendetected, and ‘1’ is inputted in the element A[1].

Further, in the case where the “continuous edge having the samedirection” exists in such a manner as to cross the radiographed objectregion, it is considered that, for example, the contour of “the jaw” hasbeen detected. Therefore, at this time, ‘1’ is inputted in the elementA[2].

{2-1-4-2b} Pattern 2: Extraction of a Concentrated Edge Line WithoutDirectivity:

{2-1-4-2b-1} First, the image is divided into a plurality of smalldomains.

{2-1-4-2b-2} In each of the divisional small domains, for the pixelsincluded in the small domain concerned, the number of pixels for whichL(x, y)=1 is satisfied is counted for every edge direction indicated byD(x, y), and it is memorized in En (n=1, 2, 3, 4), where n is made thesame as the index of the edge direction that the above-mentioned D(x, y)has. Moreover, the sum of the respective En is memorized in E0.

{2-1-4-2b-3} If a number not less than Thd3 of small domains in whichthe above mentioned E0 takes a value not smaller than a predeterminedthreshold value Thd2 exist continuously as a straight line, those smalldomains are extracted.

{2-1-4-2b-4} The En that have been extracted in the above-mentioned{2-1-4-2b-3} for the respective small domains are counted for each nvalue, and if it can be said that only edge components having aparticular direction are not so many, a pattern “concentrated edge linewithout directivity” is detected for the portion indicated by thoseextracted small domains.

For the extracted pattern, ‘1’ is inputted in the element A[3].

[2-1-4-2b-5} In the above-mentioned {2-1-4-2a-6} and {2-1-4-2b-4}, forthe means for judging whether the edge direction component having aspecified direction among a plurality of edge direction components takesa majority or not, a statistical method such as χ square testing, and amethod based on a simple judgment if the number of edge directioncomponents having a specified direction occupies a part not less than apredetermined proportion of the whole number can be cited. Further, asthe pattern to be extracted from these signal variations, on top of theabove-mentioned, the symmetry of the distribution of pixels for whichL(x, y)=1 is satisfied, and the periodicity if the edges having the samedirection appear periodically can be considered.

{2-1-4-3} Setting of an Edge Feature Amount:

On the basis of the pattern obtained by the above-mentioned patternextracting means 220, an edge feature amount can be extracted. In asimple way, it is appropriate that the value of the above mentioned A isextracted as it is.

However, by taking into consideration the number of detected patternsand the combination of the positions, a more significant feature amountcan be extracted.

For example, in the case where only one pattern representing “acontinuous edge having the same direction” exists as shown in FIG. 11(a), it can be discriminated as a particular bone of limbs (lower legbone in FIG. 11( a)) being radiographed, but in the case where aplurality of patterns exist away from one another as shown in FIG. 11(b), it is considered that a comparatively large bone such as a bone oflimbs exists for each of the positions where the respective patternsexist; that is, it can be understood that the joint to link thosecomparatively large bones is also radiographed.

Further, if a “concentrated edge line without directivity” and a“continuous edge having the same direction” are exist in an orthogonalmanner, it is considered that the possibility of the object being thecervical spine is high.

As described in the above, it is possible to recognize a radiographyobject directly by the combination of the detected patterns. Therefore,it is desirable that, as a feature amount, a different value is given toeach of the above-mentioned combination of patterns to make thecombination recognizable, and the value is extracted as a featureamount.

Therefore, in the edge feature amount setting means 230, feature amountsare set as follows, and values are set in the elemental vector C1 of thefeature vector P.

[M2]

(No mathematical expression is presented in the Japanese specification.)

Value of A value of element Name Region supposed 1) A = {1, 0, x, 0}: C1= (1, 0, 0, 0, 0, 0) “L1” humerus, femur, etc. 2) A = {2, 0, x, 0}: C1 =(0, 1, 0, 0, 0, 0) “L2” elbow joint, knee joint, etc. 3) A = {0, 1, x,x}: C1 = (0, 0, 1, 0, 0, 0) “0” thorax, thoracic spine, etc. 4) A = {0,0, 0, 1}: C1 = (0, 0, 0, 1, 0, 0) “T” lumbar, etc. 5) A = {0, 0, 1, 1}:C1 = (0, 0, 0, 0, 1, 0) “N” cervical spine, etc. 6) For others: C1 = {0,0, 0, 0, 0, 1) “E” head, etc.

The feature vector P, of which each of its elemental vectors C0 and C1is obtained in the above-mentioned way, is transmitted to thecorrelativity calculating means 320.

{2-2} Recording of Radiography Object Information:

In the above-mentioned radiography object information memorizing means360, n object vectors Si (i=1,2, - - - ,n), which describe the featureof the respective regions of the radiography object, and the elements ofthe region information Ii (i=1,2, - - - ,n) associated with Si arememorized.

The above-mentioned object vectors Si are vectors in which evaluationvalues of the respective elemental values of the feature vector P aredescribed, and by the correlation means to be described later, theevaluation values are read out for the correlation operation with thefeature vector P, and used as correlation values.

With respect to the object vector Si, each of its element Vj(j=0,1,2, - - - ,m) corresponding to Cj is described as a table ofevaluation values in which an evaluation value is described for each ofvalues that can be taken by Cj. For example, for the above-mentionedvector element Cs representing the shape of a radiography object, theelement Vs corresponding to it is a vector having components {a,b,c}.

Further, in the region information Ii, names such as the number and nameassociated uniquely with each of the regions of the object, and the nameof the radiographing orientation are memorized, and by referring to thenumber that the region information Ii has, the selection of the imageprocessing condition is done, while the names of the region and theradiographing orientation are used in the display by the imageprocessing condition selecting means 40 (a monitor display deviceprovided in the operation panel for example) for the final selection.

Further, in the region information, priority which is determineduniquely for each of the elements of the region information is included.The priority is represented by any one of natural numbers and ‘0’, andthe larger number represents the higher priority. Further, in the casewhere the value representing the priority is ‘0’, the component of theregion information concerned is not read out from the radiographyradiography object information memorizing means 360, regardless of theresult of the correlation operation to be described later. This priorityis set beforehand in accordance with, for example, the department ofdiagnosis and treatment of a hospital using the radiographing apparatusutilizing this invention.

In this way, it is for the following reason that the image processingcondition that can be selected beforehand is limited to a part and thepriority is set. That is, for example, in the case where a hospital forinternal diseases uses a radiographing apparatus utilizing thisinvention, it can be considered rare to carry out radiographing of bonesof the arm or the leg as the object. Therefore, in such a case, bymaking the image processing condition for the radiographing of the bonesof the arm or the leg as the object be not selected, or by making itdifficult to select the condition by lowering its priority, anunnecessary alternative is removed; therefore, the accuracy in theselection of an optimum image processing condition can be improved. Inparticular, in carrying out the discrimination for a radiograph in whichtwo radiographed regions of a radiography object are simultaneouslytaken as in radiographing the abdomen or the lumbar taken as the mainobject, this setting of the priority works effectively.

{2-3} Calculation of Correlation, Comparison of the Degree ofCorrelation, Temporary Memory:

In the correlation calculation means 320, for the object vector Si readout from the above-mentioned radiography object information memorizingmeans 360, and the feature vector P obtained by the above-mentionedfeature vector generating means 310, the degree of correlation issuccessively calculated.

Now, the degree of correlation can be obtained as the total sum Ti ofthe correlation values of the respective elements (the degree ofcorrelation, namely), which have been obtained for the elements of P andSi corresponding to each other. The degree of correlation Ti istransmitted to the correlation result comparing means 330, and iscompared with the predetermined threshold value Thd4.

In the case where the degree of correlation Ti is not less than saidthreshold value Thd4, the element of the region informationcorresponding to the object vector Si concerned is read out from theradiography object information memorizing means 360, and is memorized inthe temporary memory means 340. Then, after the correlation operationwith all the object vectors Si is finished, the elements of the regioninformation memorized in the temporary memory means 340 are transmittedto the image processing condition selecting means 40.

{2-3-1} Calculation of the Degree of Correlation:

The correlation operation between each of the elements Cj of the featurevector P and each of the elements Vj of the object vector Si is carriedout in the following way.

Because the element Cj of the feature vector P and the element Vj of theobject vector Si corresponding to Cj are both described as a vectorhaving the same number of elements, the evaluation value for Cj can betaken out from Vj by calculating tCjVj, and this is outputted as thecorrelation value.

For example, if the element Cs of the above-mentioned feature vectorrepresenting the shape of a radiography object is {0,0,1}, because thecorresponding element Vs of the object vector is defined as {a,b,c}, thecorrelation value is obtained as ‘c’. Further, if Cs is {1,0,0}, thecorrelation value becomes ‘a’.

Further, if this method of correlation is used, because the evaluationvalue can be designated for each of the object vectors, by making theevaluation value for a particular element larger, it can be set in adelicate manner, which element is to be emphasized.

For example, for the “head region”, because it can be generallydiscriminated by the feature that the “shape of the object” is of barreltype, the values of the respective elements of Vs corresponding to Cs isset in such a manner as to take larger values as compared to those ofthe other elements Vj, in order that the correlation result for theelement Cs corresponding to the “shape of the object” may become large.

Further, in the case where it is discriminated whether the object is the“finger region” or not, because the “size of the object” becomes aneffective factor of discrimination, for the object vector correspondingto the “finger region”, it can be exactly recognized by making theevaluation value for the “size of the object” larger than the otherelements.

As described in the above, by changing the weight for each of theelements of the object vector, more exact recognition can be carriedout.

Now, each of the above-mentioned object vectors Si (i=1,2,3,4,5) is setwith correspondence to each of the regions of a human body as shown inthe following expressions.

[M3]

Object vector Evaluation value region V0 V1 Corresponding S1 (50, 0, 0,20, 0, 0) (0, 0, 0, 0, 0, 50) head S2 (0, 50, 0, 10, 10, 0) (0, 0, 0,30, 50, 0) head S3 (0, 0, 0, 50, 30, 0) (0, 0, 50, 30, 10, 0) head S4(0, 0, 0, 50, 30, 0) (0, 0, 0, 50, 20, 0) head S5 (0, 0, 50, 30, 50, 0)(50, 50, 0, 0, 0, 0) head

Besides, as described in the foregoing, the values of the elementalvector C0=(a0,a1,a2,a3,a4,a5) of the feature vector P corresponds to“barrel type”, “sandglass type”, “boomerang type”, “square type”,“rectangle type”, and “others” successively from a0 to a5, and thevalues of the elemental vector C1=(b0,b1,b2,b3,b4,b5) corresponds to“L1”, “L2”, “0”, “T”, “N”, and “E” successively from b0 to b5.

At this time, it is assumed that radiographing has been carried out forthe thorax region, and as the result of extraction of the feature, thefeature vector P has been obtained.

-   P1: C0=(0,0,0,1,0,0), C1=(0,0,1,0,0,0).

The degree of correlation Ti (i=1,2,3,4,5) between feature vector P1 andthe above-mentioned respective vectors is as follows:

-   T1=0, T2=10, T3=100, T4=50, T5=30.

Further, it is assumed that radiographing has been carried out for theneck region, and the feature vector P2 has been obtained, and has valuesas shown in the following.

-   P2: C0=(0,0,0,0,1,0), C1=(0,1,0,0,0,0).

In this case, the degree of correlation Ti (i=1,2,3,4,5) between thefeature vector P2 and the above-mentioned respective object vectors isas follows:

-   T1=0, T2=100, T3=10, T4=20, T5=0.

The degrees of correlation Ti obtained in the above-mentioned manner aretransmitted to the correlation result comparing means 330.

{2-3-2} Comparison of the Result of Correlation:

In the correlation result comparing means 330, the degree of correlationTi is compared with the predetermined threshold value Thd4. Now,assuming that the threshold value Thd4=50, in the case of theabove-mentioned feature amount P1, because T3, T4≧thd4, the elements ofthe region information of the thorax region and the abdomen regioncorresponding to S3 and S4 are memorized in the temporary memory means340, and after that, they are transmitted to the image processingcondition selecting means 40.

Further, in the case of the above-mentioned feature vector P2, becauseT2≧Thd4, the element of the region information of the neck regioncorresponding to S2 is memorized in the temporary memory means 340, andafter that, it is transmitted to the image processing conditionselecting means 40.

For the setting value of this threshold value Thd4, it is desirable thatit is set at a value such that 1 to 5 elements of the region informationas a criterion for every radiographing can be selected. If the thresholdvalue is set at too high a value, it often occurs that the degree ofcorrelation Ti cannot exceed the threshold value for all of the objectvectors, and as the result, a suitable image processing condition cannotbe presented to a user.

On the contrary, if the threshold value is set at too low a value itcannot be avoided to present many image processing conditions to a user,which makes it impossible to fulfil the object of this invention toreduce the burden of a user by presenting automatically an imageprocessing condition that is considered suitable.

For another method of making up the correlation result comparing means330, such one as described below can be cited. That is, it is a methodsuch that, for all of the degrees of correlation Ti obtained, bycomparing the values with one another, the elements of the regioninformation corresponding to only a certain number of them (1 to 5 orso) including the one judged as having the highest degree of correlationand the successive ones in the order of the degree of correlation aretransmitted to the image processing condition selecting means 40.According to this method, a certain number of image processingconditions can be always presented to a user. Further, as described inthe above, also in the case where the elements of the region informationare selected by comparing the degrees of correlation with one another,it is also possible that by specifying the above-mentioned thresholdvalue Thd4 beforehand, the elements of the region information, for whichonly values of the degree of correlation that cannot exceed thethreshold value Thd4 have been obtained, are made not to be transmittedto the image processing selecting means 40. By doing this way, it can bereduced the possibility that an unsuitable image processing conditionbased on the result of an erroneous recognition of the radiographyobject.

{2-4} According to the method described in the foregoing, it is possibleto obtain the object region information; however, as another method ofmaking up the discriminating means, a method using pattern matching or amethod using a neural network can be considered.

{3} Selection of Image Processing Condition, Memorizing Image ProcessingConditions, and Image Processing:

The image processing selecting means 40 reads out one or a plurality ofthe image processing conditions corresponding to the region informationwhich has been transmitted from the discriminating means 30 from theimage processing condition memorizing means 50. Then, the read out imageprocessing conditions are displayed on a display means or the like so asto make it possible for a user to select one.

In other way, a part or all of the read out image processing conditionsare transmitted to the image processing means 60 beforehand, and in eachof these image processing conditions, image processing for the originalimage is carried out. The processed images obtained as the result ofthat are transmitted back to the image processing condition selectingmeans 40, and the processed images are displayed together with the imageprocessing conditions to the user.

Then, the image processing condition selected by the selection of theuser is transmitted to the image processing means 60. In the imageprocessing means 60, for the original image that has been transmittedfrom the radiation image generating means 10, image processing iscarried out using this image processing condition, and a final outputimage is obtained. For the image processing to be practiced, gradationconversion, edge emphasizing, equalization processing,enlargement/reduction processing, and combinations of these can becited.

The above-mentioned image processing condition is composed of processingdesignating information indicating which one among the above-mentionedimage processing items is to be done, and a group of parameters that isnecessary for practicing the processing. For example, in the case wherethe image processing to be practiced is gradation conversion, a look-uptable (LUT) to be utilized for converting the pixel signal values of anoriginal image into pixel signal values of output image is in it, foredge emphasizing processing, a parameter indicating the spatialfrequency to emphasize and a parameter indicating the degree ofenhancement are included.

Further, in the above-mentioned image processing condition, an indirectparameter which does not directly determines the image processing isalso included. For examples of this indirect parameter, one thatdesignates the processing for preparing an LUT for carrying outgradation conversion processing, and the standard signal value of theoriginal image that is necessary for preparing an LUT. The reason whysuch an indirect parameter is necessary is that the LUT for producing anoptimum output image becomes different depending on the conditions suchas the physical constitution of the patient to become the radiographyobject and the dose of the radiation when the radiographing ispracticed.

In the example shown in FIG. 13, the image processing conditionselecting means 40 is integral with the operation screen, and carriesout image processing using the image processing condition only that hasbeen given the largest correlation result by the above-mentioneddiscriminating means 30, or in other words, is considered most suitable;the processed image or a simplified image for confirmation obtained byreducing the number of pixels of the processed image is displayed on theoperation screen. Further, in order to make the image processingcondition used in the processing recognizable at a glance, the name ofthe image processing condition, and whether image rotation or imageinversion is included or not are displayed on the same screen.

Then, if the processed image is an image that has been subjected toimage processing suitable for the user, the processing is settled bypressing the button with the indication “OK”.

Further, among the image processing conditions which have beentransmitted from the above-mentioned discriminating means 30, ones thathave not been used in the practice of processing are displayed as “Nextsuitable buttons” indicating the names of processing (“thoracic spineAP”, “ribs”, etc. in FIG. 13).

If the above-mentioned processed image is not an image suitable for theuser, by selecting any one of these “Next suitable buttons”, the imageprocessing condition corresponding to the selected “Next suitablebutton” is transmitted to the image processing means 60, and imageprocessing is practiced in accordance with this image processingcondition.

At this time, an image that has been processed again is displayed inplace of the image displayed before, which makes it possible for theuser to confirm the processing at a glance (refer to FIG. 14). Then, inthe same way as the above-mentioned, if it is an image that is subjectedto image processing desirable for the user, by pressing the button withthe indication “OK”, the processing is settled.

Further, in the case where the operation screen has a sufficient space,it is also appropriate that, in connection with above-mentioned “Nextsuitable buttons”, images subjected to the processing based on the imageprocessing conditions corresponding to respective “Next suitablebuttons” are displayed.

Further, it is also possible to make the structure such one that, foreach of the image processing conditions that have been read out from theimage processing condition memorizing means 50, a processed image isproduced beforehand by applying the processing to the above-mentionedoriginal image by using the image processing means 60, those processedimages are displayed on the image selecting means 70, and the oneconsidered most suitable is selected by looking at those displayedimages for comparison.

An example of practice of this is shown in FIG. 15. In the case of sucha structure, because a processed image is selected as it is confirmeddirectly, it is not necessary to display the respective image processingconditions particularly.

Further, the image processing conditions based on the region informationobtained from the discriminating means 30 do not always include theimage processing condition that the user desires. Therefore, it isdesirable that, in the image processing condition selecting means 40,there is provided means for making it possible for a user to select anarbitrary image processing condition out of all the image processingconditions that are prepared beforehand, regardless of the imageprocessing conditions based on the region information obtained by thediscriminating means 30. For example, it is appropriate that, bypressing the button with the indication “Condition” in FIG. 13, a listof image processing conditions prepared beforehand are displayed, and auser can select an arbitrary image processing condition out of them.

As explained in detail up to now, according to this invention, for aradiation image, by recognizing the radiographed region of a radiographyobject and the radiographing orientation through reading the image, itbecomes possible that one or a plurality of proper image processingconditions regarded as suitable ones are selected and presented, and byselecting an optimum condition out of the presented image processingconditions, an image that is most suitable for diagnosis is obtainedwithout a troublesome operation.

1. A medical image processing apparatus, comprising: a feature vectorproducing section to analyze radiation image data including radiographedobject image data corresponding to a radiographed object with pluraldifferent analyzing methods, to extract plural different features of theradiographed object image data and to produce a radiographed objectimage data feature vector constructed with vector element of theextracted plural different features, wherein the plural differentfeatures include an external contour feature representing a feature ofan external contour of a body part comprising at least one of a head, anabdomen and a leg and an edge distribution feature representing afeature of an edge distribution pattern indicating signal variationsamong neighboring pixels; a body part feature vector memorizing sectionto memorize plural body part feature vectors of plural different bodyparts in advance, wherein each of the plural body part feature vectorsincludes an external contour feature and an edge distribution feature;and a discriminating section to obtain a correlation degree between theradiographed object image data feature vector and each of the pluralbody part feature vectors and discriminate a body part corresponding tothe radiographed object image data on the basis of the correlationdegree for each of the plural different body parts.
 2. The medical imageprocessing apparatus of claim 1, wherein the discriminating sectiondetermines a body part having the highest correlation degree among theplural correlation degrees as the body part corresponding to theradiographed object image data.
 3. The medical image processingapparatus of claim 1, wherein the feature vector producing sectionextracts each feature with a feature amount and the discriminatingsection obtains the plural correlation degrees by providing the featureamount of each feature with a different weight factor in accordance withthe kind of each of the plural different body parts.
 4. The medicalimage processing apparatus of claim 1, further comprising: aradiographed object image region extracting section to extract aradiographed object image region corresponding to the radiographedobject from the radiation image data corresponding to a radiographedregion.
 5. The medical image processing apparatus of claim 4, whereinthe feature vector producing section extracts at least two features fromthe radiographed object image data corresponding to the radiographedobject image region.
 6. The medical image processing apparatus of claim4, wherein the feature vector producing section extracts the size of theradiographed object image region as one of the features.
 7. The medicalimage processing apparatus of claim 4, wherein the feature vectorproducing section extracts the shape of the radiographed object imageregion as one of the features.
 8. The medical image processing apparatusof claim 4, wherein the feature vector producing section extracts acharacteristic value calculated on a basis of a signal variation betweenneighboring pixels as one of the features.
 9. The medical imageprocessing apparatus of claim 8, wherein the characteristic valuecalculated on a basis of the signal variation is an edge distribution.10. The medical image processing apparatus of claim 9, wherein the edgedistribution includes an edge orientation and an edge intensity.
 11. Themedical image processing apparatus of claim 1, further comprising: animage processing section to determine a processing condition on thebasis of the discriminated body part and to conduct an image processingfor the radiation image data on a basis of the processing condition. 12.The medical image processing apparatus of claim 11, wherein thediscriminating section discriminates a radiographing orientation withreference to the discriminated body part corresponding to theradiographed object.
 13. The medical image processing apparatus of claim12, wherein the image processing section comprises an image processingcondition memorizing section to memorize plural image processingconditions corresponding to at least one of the plural different bodyparts and plural radiographing orientations.
 14. The medical imageprocessing apparatus of claim 12, wherein the image processing sectioncomprises a display section to indicate the image processing conditionand a medical image corresponding to the processed radiation image data.15. The medical image processing apparatus of claim 14, wherein theimage processing section comprises a selecting section to select anarbitrary image processing condition from plural image processingconditions indicated on the display section.
 16. The medical imageprocessing apparatus of claim 15, wherein the image processing sectionindicates plural medical images corresponding to plural sets ofradiation image data obtained by plural image processing condition onthe display section and an arbitrary image pxocessing condition isselected with the selecting section on the basis of the plural medicalimages indicated on the display section.
 17. The medical imageprocessing apparatus of claim 15, wherein the image processing sectionindicates a name to specify the image processing.
 18. The medical imageprocessing apparatus of claim 15, wherein the image processing sectionindicates the necessity of image rotation and the necessity of imageinversion together with the image processing condition.
 19. A medicalimage processing apparatus, comprising: a feature vector producingsection to analyze radiation image data including radiographed objectimage data corresponding to a radiographed object with plural differentanalyzing methods, to extract plural different features of theradiographed object image data and to produce a radiographed objectimage data feature vector constructed with vector elements of theextracted plural different features; a body part feature vectormemorizing section to memorize plural body part feature vectors ofplural different body parts in advance; and a discriminating section toobtain a correlation degree between the radiographed object image datafeature vector and each of the plural body part feature vectors anddiscriminate a body part corresponding to the radiographed object imagedata on the basis of the correlation degree for each of the pluraldifferent body parts, wherein the feature vector producing sectionextracts each feature with a feature amount and the discriminatingsection obtains the plural correlation degrees by providing the featureamount of each feature with a different weight factor in accordance withthe kind of each of the plural different body parts.
 20. The medicalimage processing apparatus of claim 19, wherein the discriminatingsection determines a body part having the highest correlation degreeamong the plural correlation degrees as the body part corresponding tothe radiographed object image data.
 21. The medical image processingapparatus of claim 19, further comprising: a radiographed object imageregion extracting section to extract a radiographed object image regioncorresponding to the radiographed object from the radiation image datacorresponding to a radiographed region.
 22. The medical image processingapparatus of claim 19, wherein the feature vector producing sectionextracts at least two features from the radiographed object image datacorresponding to the radiographed object image region.
 23. The medicalimage processing apparatus of claim 22, wherein the feature vectorproducing section extracts the size of the radiographed object imageregion as one of the features.
 24. The medical image processingapparatus of claim 22, wherein the feature vector producing sectionextracts the shape of the radiographed object image region as one of thefeatures.
 25. The medical image processing apparatus of claim 22,wherein the feature vector producing section extracts a characteristicvalue calculated on a basis of a signal variation between neighboringpixels as one of the features.
 26. The medical image processingapparatus of claim 25, wherein the characteristic value calculated on abasis of the signal variation is an edge distribution.
 27. The medicalimage processing apparatus of claim 26, wherein the edge distributionincludes an edge orientation and an edge intensity.